Background: Multidisciplinary Tumor Councils (MDTs) are vital platforms that provide tailored treatment plans for cancer patients by combining expertise from various medical disciplines. Recently, Artificial Intelligence (AI) tools have been investigated as decision-support systems within these councils. Methods: In this prospective study, the compatibility of AI (ChatGPT-4.0) with MDT decisions was evaluated in 100 cancer patients presented to the tumor council between November 2024 and January 2025. AI-generated treatment recommendations based on anonymized, detailed clinical summaries were compared with real-time MDT decisions. Cohen’s Kappa and Spearman correlation tests were used for statistical analysis. Results: Neoadjuvant treatment (45%) and surgery (36%) were the most frequent MDT decisions. AI recommended surgery (39%) and neoadjuvant treatment (37%) most frequently. A high concordance rate of 76.4% was observed between AI and MDT decisions (κ = 0.764 [95% CI; 0.658–0.870] p < 0.001, ρ = 0.810 [95% CI; 0.729–0.868], p < 0.001). Most inconsistencies arose in cases requiring individualized decisions, indicating AI’s current limitations in incorporating contextual clinical judgment. Conclusion: AI demonstrates substantial agreement with MDT decisions, particularly in cases adhering to standardized oncological guidelines. However, for AI integration into clinical workflows, it must evolve to interpret real-time patient data and function transparently within ethical and legal frameworks.

Foreign body ingestion (FBI) in adults represents a clinically relevant emergency requiring endoscopic management in selected cases. Predictors of procedur

Peptic ulcer perforation remains a life-threatening surgical emergency associated with considerable postoperative mortality. Identifying reliable preoperat

Acute diverticulitis is an inflammatory process associated with pericolonic inflammation involving one or more colonic diverticula and is usually classified as uncomplicated or complicated. This multicenter study retrospectively examined the data of 135 patients who received inpatient treatment for diverticulitis in two tertiary hospitals. Some inflammation scores were calculated with the data obtained from the patients' Complete blood count(CBC) and biochemical laboratory results, and the power of these scores in predicting complicated diverticulitis was examined. A total of 135 patients (57 men and 78 women) were included in this study. In the ROC analysis performed to evaluate the effectiveness of predicting complications, the CRP/Albumin(CAR) score was found to be significant (AUC: 0.762, p = 0.001). For a cutoff value > 0.25, CAR predicted complications with 73% sensitivity and 76% specificity (AUC: 0.762, p = 0.001). Determining the severity of the disease by differentiating complicated from uncomplicated cases will contribute to reducing hospital stay times and related costs. Our study showed that CAR score can be used as a reliable, inexpensive, and rapid marker for predicting complicated diverticulitis in acute diverticulitis.

Bozkaya M, et al. Ann Surg Treat Res. 2025 Jan;108(1):64-70. https://doi.org/10.4174/astr.2025.108.1.64